A balance between supply and demand must be determined to meet the parking requirements of motorists. The ability to efficiently allocate and manage on-street parking remains elusive even when parking requirements are significant, recurring, and known ahead of time. For instance, urban parking spaces characteristically undergo periods of widely skewed demand and utilization, with low demand and light use in some periods, often during the night, and heavy demand and use at other times. Real-time parking occupancy detection systems are an emerging technology in parking management.
A parked vehicle detector is a critical element for the success of a VPODS. Such a vehicle detector needs to detect vehicles with a high accuracy (e.g., >95%) for all poses and under all conditions. To achieve this requirement, one strategy involves the use of multiple vehicle detectors, wherein each vehicle detector corresponds to and is only operative under some constrained conditions (e.g., limited to one camera pose and day time only). A parked vehicle detector in a VPODS can then be retrained to achieve such accuracy when it is operative only under these constrained conditions. When and if a different set of constrained conditions occurs, a retraining process can be conducted to generate a new parked vehicle detector to be operative under the new set of constrained conditions. This re-training approach, however, is typically costly in time and labor since identification and cropping of parked vehicles must be performed manually over a sufficiently long period of time.
Additionally, conventional image segmentation techniques for use in vehicle detection typically segment an image into many pieces based on features such as color, texture, etc. Such an approach may not include the use of semantic meanings (e.g., a car, a motorcycle, or even a bicycle) unless a further higher-level grouping and refinement operation is applied. Such image segmentation techniques are not suitable for vehicle detection in real-time applications in a VPODS since their computation is slow and performance is poor. Such an approach is also not accurate when utilized for collecting training samples.
Based on the foregoing, it is believed that a need exists for improved methods and systems for automatically training a parked vehicle detector for large deployment, as will be described in greater detail herein.